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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Aerodynamic Conceptual Design of Boundary Layer Ingestion Propulsor Systems: A Quasi-2D Through Flow Analysis Method and Multi-Fidelity Propulsor Design Framework

A propulsor design framework for maximizing the benefits of boundary layer ingestion is presented. The performance of BLI is strongly affected by all propulsor components, including the boundary layer characteristics (displacement thickness and form factor) of the ingested boundary layer at the inlet, the radical loading characteristics if the fan and exit guide vane (EGV), the area contraction from inlet to nozzle, and flow expansion at the exhaust cone. A strategy and it's associated multi-fidelity design framework are proposed for an efficient conceptual design of the BLI proplusor which inherently differs from the conventional engine. In the frame work, a quasi-2D through flow model served as the underlying fidelity model is introduced to incorporate the radial effect of the boundary layer entering the propulsor. Multi-fidelity design work is conducted to maximize the predefined performance metrics. On top of this efficient quasi-2D model. computational fluid dynamics based 3-D propulsor models are implemented to refine and validate the design. A BLI propulsion system integrated with fuselage is designed to showcase the framework. The performance of the resulting BLI propulsor system is evaluated via body-force model in unstructured Reynolds-Averaged Navier-Stoke (RANS) CFD and improvement is presented.

Lee, B. J.↗
NASA NTRS2018

Energy Analysis Method for Hidden Damage Detection

A method of detecting internal defects in composites or other multilayer materials includes generating a wavefield on a surface of the material. Wavefield data is collected from the wavefield on the surface, and the measured wavefield data is processed to provide measured energy data. The method may include generating simulated or predicted energy data for the multilayer material that is compared to the simulated energy data to determine if the multilayer material has internal defects or damage below the surface. The method can be utilized to detect and/or quantify damage or other defects that are "hidden" by damage that is closer to the surface of the material.

Campbell Leckey, Cara A.↗
NASA NTRS2018

ASTM E399 Standard Development - KIsi Analysis Method

The ASTM E399 test method is used to determine the plane‐strain fracture toughness, K (sub IC), for metallic materials. The test consists of monotonically loading a pre-cracked test specimen to failure while recording the applied load and the crack mouth opening displacement. Several test specimen types are allowable per ASTM E399; two common geometries are the compact tension (C(T)) and the single-edge bend (SE(B)) geometries. The test result is an estimate of the plane‐strain fracture toughness, K (sub q), determined by the intercept of the test record with a 95 percent secant-offset construction line. The 95 percent offset represents crack extension corresponding to approximately 2 percent of the original specimen ligament (assuming that all compliance change is the result of crack extension). If the test meets certain validity requirements based on specimen size and test behavior, the result is valid, and K (sub q) equals K (sub IC).

Tilson, William G.↗
NASA NTRS2018

Coupling Fractional Thermal Runaway Calorimetry (FTRC) Results with Statistical Analysis Methods

Important factors to consider when optimizing Lithium-ion (Li-ion) battery thermal management systems, from a battery safety perspective, include the total energy released during a single cell thermal runaway (TR) event and the fraction of the total energy that is released through the cell casing versus through the ejecta material. A fractional thermal runaway calorimeter (FTRC) was used to examine the TR behavior of the following 18650-format Li-ion cells: (1) Molicel 18650-J, (2) LG 18650-HG2, (3) Samsung 18650-30Q, (4) 3.35 Ah LG 18650 test cell, (5) Sony 18650-VC7, and (6) LG 18650-MJ1. These cells were intentionally selected to represent a variety of manufacturers, chemistries, capacities, and safety features. Because of the large number of random and non-random variables associated with the FTRC results, a linear regression model, developed using engineering statistical methods, was utilized to provide final interpretation of the TR behavior for each cell type and design variable combination.

Walker, William↗
NASA NTRS2019

Structural Analysis Methods for the Roll-Out Solar Array Flight Experiment

The Roll-Out Solar Array (ROSA) flight experiment was launched to the International Space Station (ISS) on June 3rd, 2017. ROSA is an innovative, lightweight solar array with a flexible substrate that makes use of the stored strain energy in its composite structural members to provide deployment without the use of motors. This paper discusses the effort to model the structural dynamics of ROSA using finite element modeling. Two distinct and agnostic approaches were used by separate teams to assess the structural dynamics of the solar array prior to ground vibrational testing and flight testing. Results from each approach are compared to measured dynamics from accelerometers and photogrammetry data gathered on orbit. Advantages and disadvantages of each approach are discussed as are preliminary efforts to calibrate the models to the empirical data for the benefit of future modeling efforts on similar space structures.

Chamberlain, Matthew K.↗
NASA NTRS2022

Introduction to Analysis Methods for Big Earth Data

Big Earth Data are too big to be tractable to simple data inspection. Thus, they typically require models to make sense of all the data. Useful models for Big Earth Data may be physical, statistical, or machine learning based. While physical models are ideal for understanding the data, they are not always feasible, particularly when our ability to observe at finer scales exceeds our ability to incorporate the physics. Statistical models are more generalized, but computationally intensive for many Earth Observation datasets. Machine Learning models generally scale well but are sometimes limited in the physical understanding they can offer. Hybrid models combine attributes—and advantages—of two or more of these types.

Christopher Lynnes↗